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Client

PydanticAI can act as an MCP client, connecting to MCP servers to use their tools.

Install

You need to either install pydantic-ai, orpydantic-ai-slim with the mcp optional group:

pip install 'pydantic-ai-slim[mcp]'

uv add 'pydantic-ai-slim[mcp]'

Note

MCP integration requires Python 3.10 or higher.

Usage

PydanticAI comes with two ways to connect to MCP servers:

Examples of both are shown below; mcp-run-python is used as the MCP server in both examples.

SSE Client

MCPServerSSE connects over HTTP using the HTTP + Server Sent Events transport to a server.

Note

MCPServerSSE requires an MCP server to be running and accepting HTTP connections before calling agent.run_mcp_servers(). Running the server is not managed by PydanticAI.

Before creating the SSE client, we need to run the server (docs here):

run_sse_server.py
npx @pydantic/mcp-run-python sse
mcp_sse_client.py
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerSSE

server = MCPServerSSE(url='http://localhost:3001/sse')  # (1)!
agent = Agent('openai:gpt-4o', mcp_servers=[server])  # (2)!


async def main():
    async with agent.run_mcp_servers():  # (3)!
        result = await agent.run('How many days between 2000-01-01 and 2025-03-18?')
    print(result.data)
    #> There are 9,208 days between January 1, 2000, and March 18, 2025.
  1. Define the MCP server with the URL used to connect.
  2. Create an agent with the MCP server attached.
  3. Create a client session to connect to the server.

(This example is complete, it can be run "as is" with Python 3.10+ — you'll need to add asyncio.run(main()) to run main)

What's happening here?

  • The model is receiving the prompt "how many days between 2000-01-01 and 2025-03-18?"
  • The model decides "Oh, I've got this run_python_code tool, that will be a good way to answer this question", and writes some python code to calculate the answer.
  • The model returns a tool call
  • PydanticAI sends the tool call to the MCP server using the SSE transport
  • The model is called again with the return value of running the code
  • The model returns the final answer

You can visualise this clearly, and even see the code that's run by adding three lines of code to instrument the example with logfire:

mcp_sse_client_logfire.py
import logfire

logfire.configure()
logfire.instrument_pydantic_ai()

Will display as follows:

Logfire run python code

MCP "stdio" Server

The other transport offered by MCP is the stdio transport where the server is run as a subprocess and communicates with the client over stdin and stdout. In this case, you'd use the MCPServerStdio class.

Note

When using MCPServerStdio servers, the agent.run_mcp_servers() context manager is responsible for starting and stopping the server.

mcp_stdio_client.py
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerStdio

server = MCPServerStdio('npx', ['-y', '@pydantic/mcp-run-python', 'stdio'])
agent = Agent('openai:gpt-4o', mcp_servers=[server])


async def main():
    async with agent.run_mcp_servers():
        result = await agent.run('How many days between 2000-01-01 and 2025-03-18?')
    print(result.data)
    #> There are 9,208 days between January 1, 2000, and March 18, 2025.